Network resiliency is of critical importance to network operators, service providers, and associated end users. Everyone expects “always on” network connectivity and any down time can result in lost revenue, opportunities, etc. As such, various OAM instrumentation techniques are available at each network level—layer 0 or the photonic layer, layer 1 or the time division multiplexing layer, layer 2—Ethernet, MPLS, etc., layer 3—IP, etc. Operators intently monitor OAM at all of the network levels. The existing approach to the monitoring of network health is explicit and deterministic. This is not a bad thing. However, today's OAM methods typically provide knowledge about network conditions in real time. For example, a network failure is identified at the time of the failure (or a short time after) so as to initiate a protection switch and OAM information provides guidance as to where the failure has occurred for reactive maintenance purposes. Pre-forward error correction (FEC) bit error rate (BER) does provide some advanced warning of the degradation of an optical signal but the change in BER value is typically very steep and so does not provide much advanced warning. Note that the correlation of explicit OAM information from different network sources exists today with a primary objective to help suppress the many alarms that can be created after a failure, for example. Such alarm correlation techniques are still reacting to an event after it has occurred and are not being used to predict possible future issues.
Modern, high-powered computing platforms are allowing the application of data mining techniques (aka “data analytics” or “big data”) to services by helping them learn more about their subscribers' usage patterns. By processing a vast array of somewhat unrelated data associated with particulars such as usage patterns, applications, locations, client devices, etc., service providers aim to improve service value by focusing precisely on individual customer needs. In today's networks, the general area of data mining is primarily focused on the identification of patterns and trends associated with mobile network services and IP network Layers 3-7, where knowledge about IP and associated service characteristics may be most easily extracted. It is also recognized that an interesting application of data mining is its potential to predict future events—to some degree of statistical certainty—based on past trends or by correlating data sets that were previously regarded as unrelated. For example, rather than monitoring a well-defined set of data for an explicit or deterministic actionable threshold, the data analytics approach monitors (potentially unstructured) data patterns associated with historical trends and identifies probable consequences to those patterns. A network service provider may then choose to act in advance and in anticipation of such consequences to improve service value or network performance. Another example is that of police and security organizations who use similar techniques to predict the likelihood of a crime being committed.
It would advantageous to utilize data analytics in a network OAM capacity to predict network-related failures in advance.